Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE) to individualized treatment effect (ITE) estimates to support treatment decisions. In acute ischemic stroke, mechanical thrombectomy has been shown to be more effective on average than lysis in randomized controlled trials (RCTs), such as the MR CLEAN study. We aim to identify which individual patients benefit most from mechanical thrombectomy compared to lysis. The outcome of interest is the modified Rankin Scale (mRS) at three months, an ordinal measure of functional disability (0: no symptoms, 6: death). We demonstrate that causal transformation models on directed acyclic graphs (TRAM-DAG) can be used for ITE estimation after being fitted on observational MAGIC multi-center stroke patient data. To ensure comparability with the MR CLEAN population, which we use for validation, we train the TRAM-DAG on a MAGIC sub-population with NIHSS at admission >= 6, corresponding to one inclusion criterion of MR CLEAN. The fitted model is then used to estimate ITEs for stroke patients in the MR CLEAN population. While these ITE estimates cannot be confirmed experimentally, we show that their average is consistent with the trial's reported ATE. Furthermore, the ITE estimates correctly rank trial patients by their observed frequency of a good outcome (mRS at three months <= 2). These findings support the use of causal models like TRAM-DAG for personalized decision-making in stroke care and highlight their ability to bridge the gap between observational evidence and clinical trials.

翻译：急性缺血性卒中个体化医疗需从平均治疗效果（ATE）转向个体化治疗效果（ITE）估计以支持治疗决策。在急性缺血性卒中中，随机对照试验（如MR CLEAN研究）已证实机械取栓术平均而言比溶栓更有效。本研究旨在识别哪些个体患者相较于溶栓更能从机械取栓术中获益。主要结局指标为三个月改良Rankin量表（mRS）评分——一种功能性残疾等级评估（0分：无症状，6分：死亡）。我们证明，基于有向无环图的因果变换模型（TRAM-DAG）可在拟合观察性MAGIC多中心卒中患者数据后用于ITE估计。为确保与用于验证的MR CLEAN人群可比性，我们采用入院时NIHSS评分≥6（符合MR CLEAN纳入标准之一）的MAGIC亚群训练TRAM-DAG，随后利用拟合模型估计MR CLEAN人群中卒中患者的ITE值。尽管这些ITE估计值无法通过实验验证，但其均值与试验报告的ATE一致。此外，ITE估计值能根据患者良好结局（三个月mRS≤2）的观察频率正确排序试验患者。这些发现支持使用TRAM-DAG等因果模型进行卒中诊疗的个体化决策，并突显了其在弥合观察性证据与临床试验之间鸿沟方面的能力。